Safari, Samira and Takarli, Atefeh and Salarian, Mohammad and Banejad, Hossein and Heydari, Mohammad and Ghadim, Hamed Benisi (2022) Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir. POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 31 (5). pp. 4817-4827. ISSN 2083-5906, DOI https://doi.org/10.15244/pjoes/147592.
Full text not available from this repository.Abstract
Broad-crested weirs are structures used to measure and control the water flows in rivers, canals, and irrigation and drainage networks. Accurate estimation of spillway discharge is one of the most striking elements in measurement structures. So far, many researchers have studied this issue based on various experimental conditions and a specific range of optional variables. They also have presented several relations. In the present study, 113 data sets of Bos were used for applicability of Artificial Neural Network (ANN), Gene expression programming (GEP), regression models to estimate the discharge coefficient for the rectangular broad-crested weirs. The effectiveness of the models was calculated using statistical criteria, including the coefficient of determination (R2), Root Mean Square Error (RMSE), and mean absolute error ( MAE). Comparing the models showed that the ANN with the highest R-2 coefficient (0.9916), lowest RMSE = 0.0012, and MAE = 0.00052 has the best discharge coefficient estimation than GEP models, regression models, and other empirical relations for the rectangular broadcrested weirs.
| Item Type: | Article |
|---|---|
| Funders: | Faculty of Agriculture, Bu-Ali Sina University in Hamedan, HighEnd Foreign Expert Project Plan of College of Civil Engineering of Fuzhou University |
| Uncontrolled Keywords: | rectangular broad-crested weirs; discharge coefficient; ANN; GEP; regression model |
| Subjects: | T Technology > TC Hydraulic engineering. Ocean engineering |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Koh Ai Peng |
| Date Deposited: | 22 Jul 2024 08:27 |
| Last Modified: | 22 Jul 2024 08:27 |
| URI: | http://eprints.um.edu.my/id/eprint/46298 |
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